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Discovering Intrinsic Points of Interest from Spatial Trajectory Data Sources

Piekenbrock, Matthew J.
http://rave.ohiolink.edu/etdc/view?acc_num=wright1527160689990512

Abstract Details

2018, Master of Science (MS), Wright State University, Computer Science.
This paper presents a framework for intrinsic point of interest discovery from trajectory databases. Intrinsic points of interest are regions of a geospatial area that are innately derivable by the spatial and temporal aspects of trajectory data. In contrast with other definitions of a point of interest, which often require a knowledge base or external location data, intrinsic points of interest are completely data-driven. The framework unifies recent developments from the field of density level-set estimation, applied density-based clustering techniques, and common practices in spatial point pattern analysis, offering a more theoretically grounded framework towards how a point of interest may be defined. Experiments are performed comparing the results across several modern approaches to POI discovery under thousands of parameter settings, and a marked improvement in fidelity by the proposed approach is shown in both synthetic and real world data sets.
Derek Doran, Ph.D. (Advisor)
Michael Raymer, Ph.D. (Committee Member)
William Romine, Ph.D. (Committee Member)
Krishnaprasad Thirunarayan, Ph.D. (Committee Member)
88 p.
Computer Science
Clustering; Density-based clustering; point of interest discovery

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Citations

  • Piekenbrock, M. J. (2018). Discovering Intrinsic Points of Interest from Spatial Trajectory Data Sources [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1527160689990512

    APA Style (7th edition)

  • Piekenbrock, Matthew. Discovering Intrinsic Points of Interest from Spatial Trajectory Data Sources. 2018. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1527160689990512.

    MLA Style (8th edition)

  • Piekenbrock, Matthew. "Discovering Intrinsic Points of Interest from Spatial Trajectory Data Sources." Master's thesis, Wright State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1527160689990512

    Chicago Manual of Style (17th edition)

wright1527160689990512
191
© 2018, some rights reserved.Creative Commons License Discovering Intrinsic Points of Interest from Spatial Trajectory Data Sources by Matthew J. Piekenbrock is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Wright State University and OhioLINK.